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    <abstract>The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents its design, evaluates its performance, and demonstrates its usability through its Web interface, command-line interface, and Python API.</abstract>
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%0 Conference Proceedings
%T Reliable, Reproducible, and Really Fast Leaderboards with Evalica
%A Ustalov, Dmitry
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ustalov-2025-reliable
%X The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents its design, evaluates its performance, and demonstrates its usability through its Web interface, command-line interface, and Python API.
%U https://aclanthology.org/2025.coling-demos.6/
%P 46-53

Markdown (Informal)

[Reliable, Reproducible, and Really Fast Leaderboards with Evalica](https://aclanthology.org/2025.coling-demos.6/) (Ustalov, COLING 2025)

• Reliable, Reproducible, and Really Fast Leaderboards with Evalica (Ustalov, COLING 2025)

ACL

• Dmitry Ustalov. 2025. Reliable, Reproducible, and Really Fast Leaderboards with Evalica. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 46–53, Abu Dhabi, UAE. Association for Computational Linguistics.